Risk Prediction Model for Endometrial Cancer using multiple Machine Learning Algorithms and Meta-Analysis

Principal Investigator

Name
Denis Mihaies

Degrees
Undergraduate Degree in Computer Science

Institution
Brunel Univeristy

Position Title
Student

Email
1716929@brunel.ac.uk

About this CDAS Project

Study
PLCO (Learn more about this study)

Project ID
PLCO-590

Initial CDAS Request Approval
Mar 16, 2020

Title
Risk Prediction Model for Endometrial Cancer using multiple Machine Learning Algorithms and Meta-Analysis

Summary
I am doing my final year project in computer science which involves creating a risk prediction model for endometrial cancer using different machine learning algorithms and I need a dataset which contains the risk factors associated with this type of cancer, preferably, BMI, SmokingStatus, Age, Parity, Breastfeeding, HRT use, Type 2 Diabetes, Hypertension, Contraceptive Use and the diagnosis.

Aims

The aim of this project is to create a piece of software which can assist physicians to make better decisions and help patients make an informed choice about their treatment in endometrial cancer.
The objectives:
Find an accurate percentage of risk for each individual risk factor. (That was done by the meta-analysis I concluded)
Find correlations between risk factors.
Create a model which predicts patients with endometrial cancer.
Provide personalised prevention techniques to reduce risk according to the patient’s exposure to risk factors.

Collaborators

On this project I am collaborating with my supervisor Annette Payne